1. Technical Field
The invention relates generally to manufacturing measurement system analysis, and more particularly, to a method, system and program product for optimizing a fleet of measurement systems.
2. Background Art
Methodologies have been described for the assessment and maintenance of a fleet of measurement systems or tools within a manufacturer. The focus of these methodologies has been on a fleet of measurement systems for a single application. While prior approaches use measurement systems as an example, the methodologies can also be extended to processing equipment or tools. One approach to assessment includes calculating measurement system under test (MSUT) tool matching precision (TMP), in U.S. Pat. No. 7,340,374, which is hereby incorporated by reference. Once the TMPs of a fleet of measurement systems have been determined, then a fleet measurement precision (FMP) may be calculated, which is also disclosed in U.S. Pat. No. 7,340,374. Methods to reduce FMP by evaluating components of variance that comprise each measurement system's TMP may also be employed. The above-identified metrics employ two metrics besides the conventional measures of precision and offset, i.e., SISoffset and non-linearity. If TMP and/or FMP fail when qualifying measurement systems, these metrics provide guidance on what needs to be addressed on the MSUT for the given application thereby guiding the metrology engineer on where to start diagnosing for the root cause of the matching issue. Techniques for determining a root cause through noting which of the four contributors to matching, i.e., precision, offset, SISoffset and non-linearity, is highest, or the combination of metrics is highest, may also be employed, as also described in U.S. Pat. No. 7,340,734. A root cause database in which characteristics of matching issues are associated with a signature to further refine the root cause determination may also be employed, as disclosed in U.S. 2006/0195295, which is hereby incorporated by reference.
One challenge relative to optimizing a fleet of measurement systems, however, is not having a manner of normalizing different metrics for measurement systems across different applications for comparison. In particular, common practice treats all measurement systems as equivalent, assigning product to measurement systems solely based on availability. Conventional practices also employ a measurement system dedication approach in which a measurement system is dedicated to a particular application. For example, for a particularly challenging application, the best measurement system is assigned to be the only measurement system to make the measurements. Historically, the best measurement system was determined based on single measurement system precision. Unfortunately, measurement system dedication is inefficient and can actually lead to an overall degradation of the average FMP of the fleet across all applications. Having a way to normalize different measurement systems across different applications for comparison and optimizing the fleet based on the normalized values, therefore, would be helpful.